Initial import: XAUUSD news sentiment models, evals, and data tooling
TF-IDF baseline (news_sentiment.py, models/xauusd_sentiment.joblib) and the two-head DeBERTa-v3-small transformer (transformer/, news_transformer.py) with its Google News crawler and DeepSeek labeller (data_collect/), the golden-set and archive evals (eval/), and the 2026-09-22 and 2026-09-27 results and progress notes. Transformer weights (model.pt, model.onnx) and the crawl database are not tracked; rebuild with transformer/train.py and transformer/export_onnx.py. The DeepSeek label cache in data/ is tracked.
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{
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"base_model": "microsoft/deberta-v3-small",
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"trained_at": "2026-09-27T23:15:30.403442+00:00",
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"final": true,
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"max_length": 64,
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"relevant_min_abs_xau": 0.25,
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"irrelevant_max_abs_xau": 0.1,
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"relevance_threshold": 0.2,
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"best_epoch": 4,
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"hyperparameters": {
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"base": "microsoft/deberta-v3-small",
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"epochs": 4,
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"batch_size": 64,
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"lr": 5e-05,
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"warmup": 0.06,
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"weight_decay": 0.01,
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"val_from": "2026-09-18",
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"seed": 42,
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"final": true
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},
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"data": {
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"train": {
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"n": 15416,
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"relevant": 4746,
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"irrelevant": 8996,
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"grey": 1674,
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"bullish": 3080,
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"bearish": 1666,
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"sources": {
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"history": 13893,
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"newsdata": 1523
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},
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"years": {
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"2009": 10,
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"2010": 10,
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"2011": 9,
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"2012": 2,
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"2015": 11,
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"2016": 6,
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"2017": 32,
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"2018": 19,
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"2019": 949,
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"2020": 1144,
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"2021": 1197,
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"2022": 1467,
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"2023": 1619,
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"2024": 1938,
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"2025": 2922,
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"2026": 4081
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}
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},
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"dev": {
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"n": 811,
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"relevant": 251,
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"irrelevant": 476,
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"grey": 84,
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"bullish": 164,
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"bearish": 87,
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"sources": {
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"history": 732,
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"newsdata": 79
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},
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"years": {
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"2011": 1,
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"2015": 2,
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"2017": 1,
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"2019": 54,
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"2020": 67,
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"2021": 67,
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"2022": 69,
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"2023": 72,
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"2024": 109,
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"2025": 149,
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"2026": 220
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}
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},
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"val": {
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"n": 1018,
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"relevant": 378,
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"irrelevant": 501,
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"grey": 139,
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"bullish": 171,
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"bearish": 207,
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"sources": {
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"newsdata": 1018
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},
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"years": {
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"2026": 1018
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}
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}
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},
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"history": [
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{
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"epoch": 1,
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"train_loss": 0.9252183866832664,
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"seconds": 17
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},
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{
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"epoch": 2,
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"train_loss": 0.5151546665778123,
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"seconds": 34
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},
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{
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"epoch": 3,
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"train_loss": 0.3269671004340074,
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"seconds": 50
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},
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{
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"epoch": 4,
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"train_loss": 0.2127824436748985,
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"seconds": 67
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}
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],
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"evaluation": {
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"dev": {
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"relevance_threshold": 0.2,
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"relevance_precision": 0.7660256410256411,
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"relevance_recall": 0.952191235059761,
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"relevance_f1": 0.8490230905861458,
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"relevance_accuracy": 0.8830811554332875,
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"direction_accuracy_on_relevant": 0.8884462151394422,
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"three_class_vs_deepseek_0.05": 0.7891491985203453,
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"three_class_macro_f1": 0.7551648489586519,
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"n": 811
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},
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"val": {
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"relevance_threshold": 0.2,
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"relevance_precision": 0.7237113402061855,
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"relevance_recall": 0.9285714285714286,
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"relevance_f1": 0.8134414831981461,
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"relevance_accuracy": 0.8168373151308305,
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"direction_accuracy_on_relevant": 0.8941798941798942,
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"three_class_vs_deepseek_0.05": 0.712180746561886,
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"three_class_macro_f1": 0.7103112215543884,
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"n": 1018
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}
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},
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"evaluation_note": "final model trained on val too; evaluation is from the non-final run",
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"labels": "DeepSeek deepseek-v4-flash live prompt XAU score"
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}
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